def runOnListFileDPOMDP_unfinished(baseSavePath, listFilePath='DPOMDPsToEval.txt'):
# For now, can't go back to inprogress ones
pool = Pool()
pString = claimRunEnvParamSet_unfinished(listFilePath)
while pString is not None:
splitPString = pString.split('/') # {run}/{env}/{param}
run = splitPString[0]
os.makedirs(os.path.join(baseSavePath, run), exist_ok=True)
envName = splitPString[1]
params = GDICEParams().fromName(name=splitPString[2])
try:
env = gym.make(envName)
except MemoryError:
print(envName + ' too large for memory', file=sys.stderr)
return
except Exception as e:
print(envName + ' encountered error in creation', file=sys.stderr)
print(e, file=sys.stderr)
return
wasPartiallyRun, npzFilename = checkIfPartial(envName, params.name)
prevResults = None
if wasPartiallyRun:
print(params.name + ' partially finished for ' + envName + ', loading...', file=sys.stderr)
prevResults, FSCDist = loadResults(npzFilename)[:2]
else:
if params.centralized:
FSCDist = FiniteStateControllerDistribution(params.numNodes, env.action_space[0].n,
env.observation_space[0].n)
else:
FSCDist = [FiniteStateControllerDistribution(params.numNodes, env.action_space[a].n,
env.observation_space[a].n) for a in range(env.agents)]
env.reset()
try:
results = runGDICEOnEnvironment(env, FSCDist, params, parallel=pool, results=prevResults, baseDir=os.path.join(baseSavePath, run))
except MemoryError:
print(envName + ' too large for parallel processing. Switching to MultiEnv...', file=sys.stderr)
results = runGDICEOnEnvironment(env, FSCDist, params, parallel=None, results=prevResults, baseDir=os.path.join(baseSavePath, run))
except Exception as e:
print(envName + ' encountered error in runnning' + params.name + ', skipping to next param', file=sys.stderr)
print(e, file=sys.stderr)
return
saveResults(os.path.join(os.path.join(baseSavePath, run), 'EndResults'), envName, params, results)
# Remove from in progress
registerRunEnvParamSetCompletion_unfinished(pString, listFilePath)
# Delete the temp results
try:
for filename in glob.glob(os.path.join(os.path.join(baseSavePath, run), 'GDICEResults', envName, params.name) + '*'):
os.remove(filename)
except:
return
# Claim next one
pString = claimRunEnvParamSet_unfinished(listFilePath)
def runGridSearchOnAllEnvDPOMDP(baseSavePath):
pool = Pool()
envList, GDICEList = getGridSearchGDICEParams()
for envStr in envList:
try:
env = gym.make(envStr)
except MemoryError:
print(envStr + ' too large for memory', file=sys.stderr)
continue
except Exception as e:
print(envStr + ' encountered error in creation, skipping', file=sys.stderr)
print(e, file=sys.stderr)
continue
for params in GDICEList:
# Skip this permutation if we already have final results
if checkIfFinished(envStr, params.name, baseDir=baseSavePath)[0]:
print(params.name +' already finished for ' +envStr+ ', skipping...', file=sys.stderr)
continue
wasPartiallyRun, npzFilename = checkIfPartial(envStr, params.name)
prevResults = None
if wasPartiallyRun:
print(params.name + ' partially finished for ' + envStr + ', loading...', file=sys.stderr)
prevResults, FSCDist = loadResults(npzFilename)[:2]
else:
if params.centralized:
FSCDist = FiniteStateControllerDistribution(params.numNodes, env.action_space[0].n,
env.observation_space[0].n)
else:
FSCDist = [FiniteStateControllerDistribution(params.numNodes, env.action_space[a].n,
env.observation_space[a].n) for a in range(env.agents)]
env.reset()
try:
results = runGDICEOnEnvironment(env, FSCDist, params, parallel=pool, results=prevResults, baseDir=baseSavePath)
except MemoryError:
print(envStr + ' too large for parallel processing. Switching to MultiEnv...', file=sys.stderr)
results = runGDICEOnEnvironment(env, FSCDist, params, parallel=None, results=prevResults, baseDir=baseSavePath)
except Exception as e:
print(envStr + ' encountered error in runnning' + params.name + ', skipping to next param', file=sys.stderr)
print(e, file=sys.stderr)
continue
saveResults(os.path.join(baseSavePath, 'EndResults'), envStr, params, results)
# Delete the temp results
try:
for filename in glob.glob(os.path.join(baseSavePath, 'GDICEResults', envStr, params.name) + '*'):
os.remove(filename)
except:
continue
def runBasicDPOMDP():
envName = 'DPOMDP-recycling-v0'
env = gym.make(envName)
testParams = GDICEParams([10, 10])
controllers = [FiniteStateControllerDistribution(testParams.numNodes[a], env.action_space[a].n, env.observation_space[a].n) for a in range(env.agents)]
pool = Pool()
bestValue, bestValueStdDev, bestActionTransitions, bestNodeObservationTransitions, updatedControllerDistribution, \
estimatedConvergenceIteration, allValues, allStdDev, bestValueAtEachIteration, bestStdDevAtEachIteration = \
runGDICEOnEnvironment(env, controllers, testParams, parallel=pool)
def runBasic():
envName = 'POMDP-4x3-episodic-v0'
env = gym.make(envName) # Make a gym environment with POMDP-1d-episodic-v0
testParams = GDICEParams() # Choose G-DICE parameters with default values
controllerDistribution = FiniteStateControllerDistribution(testParams.numNodes, env.action_space.n, env.observation_space.n) # make a controller with 10 nodes, with #actions and observations from environment
#pool = Pool() # Use a pool for parallel processing. Max # threads
pool = None # use a multiEnv for vectorized processing on computers with low memory or no core access
# Run GDICE. Return the best average value, its standard deviation,
# tables of the best deterministic transitions, and the updated distribution of controllers
bestValue, bestValueStdDev, bestActionTransitions, bestNodeObservationTransitions, updatedControllerDistribution, \
estimatedConvergenceIteration, allValues, allStdDev, bestValueAtEachIteration, bestStdDevAtEachIteration = \
runGDICEOnEnvironment(env, controllerDistribution, testParams, parallel=pool)
# Create a deterministic controller from the tables above
bestDeterministicController = DeterministicFiniteStateController(bestActionTransitions, bestNodeObservationTransitions)
def runDomain(env, testParams):
controllerDistribution = FiniteStateControllerDistribution(
testParams.numNodes, env.action_space.n, env.observation_space.n
) # make a controller with 10 nodes, with #actions and observations from environment
# pool = Pool() # Use a pool for parallel processing. Max # threads
pool = None # use a multiEnv for vectorized processing on computers with low memory or no core access
# Run GDICE. Return the best average value, its standard deviation,
# tables of the best deterministic transitions, and the updated distribution of controllers
bestValue, bestValueStdDev, bestActionTransitions, bestNodeObservationTransitions, updatedControllerDistribution, \
estimatedConvergenceIteration, allValues, allStdDev, bestValueAtEachIteration, bestStdDevAtEachIteration = \
runGDICEOnEnvironment(env, controllerDistribution, testParams, parallel=pool, envType=1)
# Create a deterministic controller from the tables above
bestDeterministicController = DeterministicFiniteStateController(
bestActionTransitions, bestNodeObservationTransitions)
return bestDeterministicController
# -*- coding: utf-8 -*-
from GDICE_Python.Controllers import FiniteStateControllerDistribution
from GDICE_Python.Parameters import GDICEParams
from GDICE_Python.Algorithms import runGDICEOnEnvironment
from multiprocessing import Pool
import gym
if __name__ == "__main__":
envName = 'DPOMDP-recycling-v0'
env = gym.make(envName)
testParams = GDICEParams([10, 10], centralized=False)
controllers = [
FiniteStateControllerDistribution(testParams.numNodes[a],
env.action_space[a].n,
env.observation_space[a].n, True)
for a in range(env.agents)
]
pool = Pool()
bestValue, bestValueStdDev, bestActionTransitions, bestNodeObservationTransitions, updatedControllerDistribution, estimatedConvergenceIteration, allValues, allStdDev, bestValueAtEachIteration, bestStdDevAtEachIteration = runGDICEOnEnvironment(
env, controllers, testParams, parallel=pool)